PDF(Pubmed)
Abstract：
Epilepsy is a neurological disorder characterized by recurrent seizures, affecting over 65 million people worldwide. Treatment typically commences with the use of anti-seizure medications, including both mono- and poly-therapy. Should these fail, more invasive therapies such as surgery, electrical stimulation and focal drug delivery are often considered in an attempt to render the person seizure free. Although a significant portion ultimately benefit from these treatment options, treatment responses often fluctuate over time. The physiological mechanisms underlying these temporal variations are poorly understood, making prognosis a significant challenge when treating epilepsy. Here we use a dynamic network model of seizure transition to understand how seizure propensity may vary over time as a consequence of changes in excitability. Through computer simulations, we explore the relationship between the impact of treatment on dynamic network properties and their vulnerability over time that permit a return to states of high seizure propensity. For small networks we show vulnerability can be fully characterised by the size of the first transitive component (FTC). For larger networks, we find measures of network efficiency, incoherence and heterogeneity (degree variance) correlate with robustness of networks to increasing excitability. These results provide a set of potential prognostic markers for therapeutic interventions in epilepsy. Such markers could be used to support the development of personalized treatment strategies, ultimately contributing to understanding of long-term seizure freedom.
摘要：
癫痫是一种以反复发作为特征的神经系统疾病，影响全球超过6500万人。治疗通常从使用抗癫痫药物开始，包括单一疗法和多疗法。如果这些失败，更具侵入性的治疗方法，如手术，电刺激和局灶性药物递送通常被认为是为了使患者无癫痫发作。虽然很大一部分最终受益于这些治疗方案，治疗反应经常随着时间的推移而波动。这些时间变化背后的生理机制知之甚少，使预后成为治疗癫痫的重大挑战。在这里，我们使用癫痫发作过渡的动态网络模型来了解癫痫发作倾向如何随着时间的推移而随着兴奋性的变化而变化。通过计算机模拟,我们探讨了治疗对动态网络特性的影响与其随时间的脆弱性之间的关系,这些脆弱性允许患者恢复到高发作倾向状态.对于小型网络，我们表明漏洞可以通过第一个传递组件（FTC）的大小来完全表征。对于更大的网络，我们找到了网络效率的衡量标准，不相干和异质性(程度方差)与网络对增加兴奋性的鲁棒性相关。这些结果为癫痫的治疗干预提供了一组潜在的预后标志物。这些标记可用于支持个性化治疗策略的开发，最终有助于理解长期癫痫发作的自由。
